"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
a				={'LayoutLMv2Config', 'LayoutLMv3Config'}






@is_pipeline_test
class   __UpperCAmelCase		(      unittest.TestCase      ):
				A__ :  List[Any]    				=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				A__ :  Optional[Any]    				=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

				if model_mapping is not None:
								A__ :  List[Any]    				=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
				if tf_model_mapping is not None:
								A__ :  Tuple    				=			{
								    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
								}

				@require_torch
				def 						_a      ( self ):
										lowerCamelCase__   =pipeline(
										    task="text-classification"      ,					model="hf-internal-testing/tiny-random-distilbert"      ,					framework="pt" )

										lowerCamelCase__   =text_classifier("This is great !" )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "LABEL_0", "score": 0.5_0_4}] )

										lowerCamelCase__   =text_classifier("This is great !"      ,					top_k=2 )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}] )

										lowerCamelCase__   =text_classifier(["This is great !", "This is bad"]      ,					top_k=2 )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[
										        [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
										        [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
										    ]      ,					)

										lowerCamelCase__   =text_classifier("This is great !"      ,					top_k=1 )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "LABEL_0", "score": 0.5_0_4}] )

										# Legacy behavior
										lowerCamelCase__   =text_classifier("This is great !"      ,					return_all_scores=_lowerCamelCase )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "LABEL_0", "score": 0.5_0_4}] )

										lowerCamelCase__   =text_classifier("This is great !"      ,					return_all_scores=_lowerCamelCase )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]] )

										lowerCamelCase__   =text_classifier(["This is great !", "Something else"]      ,					return_all_scores=_lowerCamelCase )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[
										        [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
										        [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
										    ]      ,					)

										lowerCamelCase__   =text_classifier(["This is great !", "Something else"]      ,					return_all_scores=_lowerCamelCase )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[
										        {"label": "LABEL_0", "score": 0.5_0_4},
										        {"label": "LABEL_0", "score": 0.5_0_4},
										    ]      ,					)

				@require_torch
				def 						_a      ( self ):
										import torch

										lowerCamelCase__   =pipeline(
										    task="text-classification"      ,					model="hf-internal-testing/tiny-random-distilbert"      ,					framework="pt"      ,					device=torch.device("cpu" )      ,					)

										lowerCamelCase__   =text_classifier("This is great !" )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "LABEL_0", "score": 0.5_0_4}] )

				@require_tf
				def 						_a      ( self ):
										lowerCamelCase__   =pipeline(
										    task="text-classification"      ,					model="hf-internal-testing/tiny-random-distilbert"      ,					framework="tf" )

										lowerCamelCase__   =text_classifier("This is great !" )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "LABEL_0", "score": 0.5_0_4}] )

				@slow
				@require_torch
				def 						_a      ( self ):
										lowerCamelCase__   =pipeline("text-classification" )

										lowerCamelCase__   =text_classifier("This is great !" )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "POSITIVE", "score": 1.0}] )
										lowerCamelCase__   =text_classifier("This is bad !" )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "NEGATIVE", "score": 1.0}] )
										lowerCamelCase__   =text_classifier("Birds are a type of animal" )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "POSITIVE", "score": 0.9_8_8}] )

				@slow
				@require_tf
				def 						_a      ( self ):
										lowerCamelCase__   =pipeline("text-classification"      ,					framework="tf" )

										lowerCamelCase__   =text_classifier("This is great !" )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "POSITIVE", "score": 1.0}] )
										lowerCamelCase__   =text_classifier("This is bad !" )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "NEGATIVE", "score": 1.0}] )
										lowerCamelCase__   =text_classifier("Birds are a type of animal" )
										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": "POSITIVE", "score": 0.9_8_8}] )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =TextClassificationPipeline(model=_lowerCamelCase      ,					tokenizer=_lowerCamelCase )
										return text_classifier, ["HuggingFace is in", "This is another test"]






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =text_classifier.model
										# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
										lowerCamelCase__   ="HuggingFace is in"
										lowerCamelCase__   =text_classifier(_lowerCamelCase )

										self.assertEqual(nested_simplify(_lowerCamelCase )      ,					[{"label": ANY(_lowerCamelCase ), "score": ANY(_lowerCamelCase )}] )
										self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )

										lowerCamelCase__   =["HuggingFace is in ", "Paris is in France"]
										lowerCamelCase__   =text_classifier(_lowerCamelCase )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[{"label": ANY(_lowerCamelCase ), "score": ANY(_lowerCamelCase )}, {"label": ANY(_lowerCamelCase ), "score": ANY(_lowerCamelCase )}]      ,					)
										self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )
										self.assertTrue(outputs[1]["label"] in model.config.idalabel.values() )

										# Forcing to get all results with `top_k=None`
										# This is NOT the legacy format
										lowerCamelCase__   =text_classifier(_lowerCamelCase      ,					top_k=_lowerCamelCase )
										lowerCamelCase__   =len(model.config.idalabel.values() )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[[{"label": ANY(_lowerCamelCase ), "score": ANY(_lowerCamelCase )}] * N, [{"label": ANY(_lowerCamelCase ), "score": ANY(_lowerCamelCase )}] * N]      ,					)

										lowerCamelCase__   ={"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
										lowerCamelCase__   =text_classifier(_lowerCamelCase )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					{"label": ANY(_lowerCamelCase ), "score": ANY(_lowerCamelCase )}      ,					)
										self.assertTrue(outputs["label"] in model.config.idalabel.values() )

										# This might be used a text pair, but tokenizer + pipe interaction
										# makes it hard to understand that it's not using the pair properly
										# https://github.com/huggingface/transformers/issues/17305
										# We disabled this usage instead as it was outputting wrong outputs.
										lowerCamelCase__   =[["HuggingFace is in ", "Paris is in France"]]
										with self.assertRaises(_lowerCamelCase ):
																text_classifier(_lowerCamelCase )

										# This used to be valid for doing text pairs
										# We're keeping it working because of backward compatibility
										lowerCamelCase__   =text_classifier([[["HuggingFace is in ", "Paris is in France"]]] )
										self.assertEqual(
										    nested_simplify(_lowerCamelCase )      ,					[{"label": ANY(_lowerCamelCase ), "score": ANY(_lowerCamelCase )}]      ,					)
										self.assertTrue(outputs[0]["label"] in model.config.idalabel.values() )



"""simple docstring"""


from __future__ import annotations

import math





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if len(__lowerCAmelCase							) != 2 or len(a[0]							) != 2 or len(__lowerCAmelCase							) != 2 or len(b[0]							) != 2:
												raise Exception("Matrices are not 2x2"							)
						lowerCamelCase__   =[
						    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
						    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
						]
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Dict:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[list, list, list, list]:




						'''simple docstring'''



						if len(__lowerCAmelCase							) % 2 != 0 or len(a[0]							) % 2 != 0:
												raise Exception("Odd matrices are not supported!"							)

						lowerCamelCase__   =len(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_length // 2

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[
						    [a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)
						]

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)]

						return top_left, top_right, bot_left, bot_right





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[int, int]:




						'''simple docstring'''



						return len(__lowerCAmelCase							), len(matrix[0]							)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   None:




						'''simple docstring'''



						print("\n".join(str(__lowerCAmelCase							) for line in matrix							)							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							) == (2, 2):
												return default_matrix_multiplication(__lowerCAmelCase  ,       __lowerCAmelCase							)

						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)
						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)

						lowerCamelCase__   =matrix_addition(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_subtraction(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)

						# construct the new matrix from our 4 quadrants
						lowerCamelCase__   =[]
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(top_left[i] + top_right[i]							)
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(bot_left[i] + bot_right[i]							)
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							)[1] != matrix_dimensions(__lowerCAmelCase							)[0]:
												lowerCamelCase__   =(
												    "Unable to multiply these matrices, please check the dimensions.\n"
												    F'''Matrix A: {matrixa}\n'''
												    F'''Matrix B: {matrixa}'''
												)
												raise Exception(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)

						if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
												return [matrixa, matrixa]

						lowerCamelCase__   =max(*__lowerCAmelCase  ,       *__lowerCAmelCase							)
						lowerCamelCase__   =int(math.pow(2  ,       math.ceil(math.loga(__lowerCAmelCase							)							)							)							)
						lowerCamelCase__   =matrixa
						lowerCamelCase__   =matrixa

						# Adding zeros to the matrices so that the arrays dimensions are the same and also
						# power of 2
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Removing the additional zeros
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								final_matrix[i].pop()
												else:
																		final_matrix.pop()
						return final_matrix


if __name__ == "__main__":
	a				=[
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 2, 3, 1],
	]
	a				=[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
	print(strassen(matrixa, matrixa))


